(1) Field of the Invention
The present invention generally relates to sonar systems and more specifically to sonar systems particularly adapted for identifying the location of an underwater object(s).
(2) Description of the Prior Art
Conventional passive sonar systems detect acoustic signals emanating from an underwater object; that is, any device that moves through the water while emitting acoustic signals that sonar can detect. Torpedoes and submarines are examples of such underwater objects.
As modern, very quiet submarine platforms become operational in large numbers, new methods of detecting very low level signals from these quiet submarine platforms are desired, especially in the presence of high noise levels from surface shipping, wind biologics, and other sources of ambient noise.
Ambient noise from all these sources can be very loud, especially in coastal waters where surface ship densities are largest, and these loud noise signals can prevent the detection of weaker signals of interest. It is therefore, highly desirable to suppress the unwanted noises in order to better detect and track the signal(s) of interest. Noise must be suppressed coherently by estimating both the phases and amplitudes of the noise sources desired to be suppressed. This can be accomplished prior to the beamforming process, during the beamforming process, or in the post-beamforming process. Prior to beamforming, digital time series data sets from the hydrophone array are decimated, filtered, and heterodyned to prepare the multiple time series data sets to be transformed to the frequency domain utilizing a Fast Fourier Transform routine. Fourier transformed frequency domain data sets, commonly referred to as “hydrophone FFTs,” are then beamformed by using any one of numerous beamforming algorithms. A particular beamforming algorithm, called the Fourier Integral Method (FIM), was previously patented by the inventors in U.S. Pat. No. 5,481,505 which is incorporated by reference herein.
There have been several prior art methods developed to solve the sonar problem of reducing noise from a loud, near-surface noise source while maintaining the signal level of signals produced by the target of interest (TOI). As used herein, the phrases “near-surface noise source” or “near-surface source” refer to an object (e.g., ship) that is primarily located on or near the ocean surface. An intensive effort has been directed to the area of adaptive beamforming, as evidenced by the development of the well known minimum variance distortion response (MVDR) algorithms. For ideal ocean conditions, when the spatial coherence of the acoustic field is known exactly, MVDR algorithms are optimum in minimizing the total noise field while maintaining the target of interest's signal level constant. However, there is only a finite time to estimate the acoustic field spatial coherence. Furthermore, errors between the actual and estimated acoustic field spatial coherence degrade the performance of MVDR algorithms rapidly because MVDR algorithms are highly non-linear. MVDR algorithms require the calculation of the inverse matrix of the acoustic field spatial coherence spectral matrix (CSM). Small errors in the estimate of CSM can propagate to become very large errors in the estimate of the inverse matrix of CSM. The CSM is defined as the matrix of all cross-product pairs of individual hydrophone time series Fast Fourier Transforms (FFTs). The CSM is described in detail in commonly owned U.S. Patent No. 5,481,505. Therefore, MVDR algorithms are not robust in realistic open ocean environments, and are severely degraded when short averaging times must be used in tactical sonar systems.
A second class of prior art algorithms developed to address the aforementioned problem is referred to as the WHISPR family of processing algorithms. The WHISPR-related algorithms are relatively large. These algorithms rely on one physical principle: the acoustic time series of a near-surface noise source has a significantly greater time variance than the acoustic time series from a submerged target of interest due to the Lloyd's Mirror effect and several other causes. Although WHISPR has shown some promise on selected acoustic data sets, it has never been developed into a real time system because it is not robust in real ocean environments.
U.S. Pat. No. 6,424,596 is directed to a method and system for significantly reducing the acoustic noise from near-surface sources using an array processing technique that utilizes multiple signal classification (MUSIC) beamforming and the Lloyd's Mirror interference pattern at very low frequencies. Noise from nearby near-surface sources, such as merchant ships, super tankers, fishing trawlers, seismic profiling platforms, or other sources near the ocean surface can significantly interfere with the detection and tracking of a quiet target-of-interest (TOI) located well below the ocean surface. The '596 invention reduces the noise of the near-surface sources, without degrading the signal level and quality of the TOI, by utilizing a unique application of the MUSIC beamforming process to separate the noise and signal subspace. Next, eigenvalue beamforming is used to reduce narrowband energy in selected frequency bins wherein the near-surface noise is radiating. Next, predetermined frequency and magnitude variance parameters are used to eliminate broadband noise emanating from the near-surface sources.
However, these prior inventions do not allow a sonar system to selectively reduce sound emanating from an object at a known azimuth and bearing in order to enhance sound emanating from an object of interest.